DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJDMTA provides online manuscript tracking system.

Every issue of Journal of IJDMTA is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJDMTA special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJDMTA website. For complete procedure, contact us at admin@iirgroups.org
Paper Template
Copyright Form
Subscription Form
web counter
web counter
Published in:   Vol. 3 Issue 1 Date of Publication:   June 2014

An Analysis of Data Mining Applications for Fraud Detection in Securities Market

S.Dhanalakshmi,C.Subramanian

Page(s):   9- 15 ISSN:   2278-2397
DOI:   10.20894/IJDMTA.102.003.001.003 Publisher:   Integrated Intelligent Research (IIR)


  1. Han, J. and Kamber M,   "Data Mining Concepts and Techniques",   Morgan Kaufmann   ,2006
    View Artical

  2. Bose, N.K. and Liang, P,   "Neural Networks Fundamental With Graphs Algorithms And Applications",   Mcgraw-Hill: New York, NY   ,1996
    View Artical

  3. Delmater R., and Handcock M,   "Data Mining Explained: A Manager?s Guide to Customer- Centric Business Intelligence",   Digital Press, Boston   ,2001
    View Artical

  4. J. Neville, D. Jensen, J. Komoroske, K. Palmer, H. Goldberg,   "Using Relational Knowledge Discovery to Prevent Securities Fraud",   11th ACM SIGKDD International Conference on Knowle   ,2005
    View Artical

  5. Guilford, J. P,   "The nature of human intelligence",   New York:McGraw-Hill   ,1967
    View Artical

  6. Hertz, J.K,   "Introduction to the Theory Of Neural Computation",   Addison ?Wesley: New York, NY.   ,1991
    View Artical

  7. Phua, V. Lee, K. Smith, and R. Gayler,   "A comprehensive survey of data mining-based fraud detection research",   Artificial Intelligence Review   ,2005
    View Artical

  8. Caruana, R. & Niculescu-Mizil,   "Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria",   Proc. of SIGKDD04   ,2004
    View Artical

  9. T. Fawcett, F. Provost,   "Activity monitoring: Noticing Interesting Changes in Behavior",   In proceedings of SIGKDD99   ,1999
    View Artical

  10. S. Viaene, R. Derrig, G. Dedene,   "A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis",   IEEE Transactions on Knowledge and Data Engineerin   ,Vol.16   ,2004
    View Artical

  11. W. Lee, D. Xiang.,   "Information-theoretic Measures for Anomaly Detection",   In proceedings of 2001 IEEE Symposium on Security    ,2001
    View Artical

  12. K. Yamanishi, J. Takeuchi, G. Williams, P. Milne.,   "On- Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms",   Journal of Data Mining and Knowledge Discovery   ,Vol.8   ,2004
    View Artical

  13. P. Burge, J. Shawe-Taylor,   "An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in F",   Journal of Parallel and Distributed Computing   ,Vol.61   ,2001
    View Artical

  14. R. Bolton, D. Hand.,   "Unsupervised Profiling Methods for Fraud Detection",   In proceeding of Credit Scoring and Credit Control   ,2001
    View Artical

  15. P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth.,   "CRISP-DM 1.0 Step-by-step data mining guide",   The CRISP-DM consortium   ,2000
    View Artical